AI Tool Helps Predict Relapse of Pediatric Brain Cancer

Artificial intelligence (AI) shows tremendous promise for analyzing vast medical imaging datasets and identifying patterns that may be missed by human observers. AI-assisted interpretation of brain scans may help improve care for children with brain tumors called gliomas, which are typically treatable but vary in risk of recurrence. Investigators from Mass General Brigham and collaborators at Boston Children’s Hospital and Dana-Farber/Boston Children’s Cancer and Blood Disorders Center trained deep learning algorithms to analyze sequential, post-treatment brain scans and flag patients at risk of cancer recurrence. Their results are published in The New England Journal of Medicine AI.

"Many pediatric gliomas are curable with surgery alone, but when relapses occur, they can be devastating," said corresponding author Benjamin Kann, MD, of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham and the Department of Radiation Oncology at Brigham and Women’s Hospital. "It is very difficult to predict who may be at risk of recurrence, so patients undergo frequent follow-up with magnetic resonance (MR) imaging for many years, a process that can be stressful and burdensome for children and families. We need better tools to identify early which patients are at the highest risk of recurrence."

Studies of relatively rare diseases, like pediatric cancers, can be challenged by limited data. This study, which was funded in part by the National Institutes of Health, leveraged institutional partnerships across the country to collect nearly 4,000 MR scans from 715 pediatric patients. To maximize what AI could “learn” from a patient's brain scans -  and more accurately predict recurrence - the researchers employed a technique called temporal learning, which trains the model to synthesize findings from multiple brain scans taken over the course of several months post-surgery.

Typically, AI models for medical imaging are trained to draw conclusions from single scans; with temporal learning, which has not previously been used for medical imaging AI research, images acquired over time inform the algorithm’s prediction of cancer recurrence. To develop the temporal learning model, the researchers first trained the model to sequence a patient’s post-surgery MR scans in chronological order so that the model could learn to recognize subtle changes. From there, the researchers fine-tuned the model to correctly associate changes with subsequent cancer recurrence, where appropriate.

Ultimately, the researchers found that the temporal learning model predicted recurrence of either low- or high-grade glioma by one-year post-treatment, with an accuracy of 75-89 percent - substantially better than the accuracy associated with predictions based on single images, which they found to be roughly 50 percent (no better than chance). Providing the AI with images from more timepoints post-treatment increased the model’s prediction accuracy, but only four to six images were required before this improvement plateaued.

The researchers caution that further validation across additional settings is necessary prior to clinical application. Ultimately, they hope to launch clinical trials to see if AI-informed risk predictions can result in improvements to care - whether by reducing imaging frequency for the lowest-risk patients or by preemptively treating high-risk patients with targeted adjuvant therapies.

"We have shown that AI is capable of effectively analyzing and making predictions from multiple images, not just single scans," said first author Divyanshu Tak, MS, of the AIM Program at Mass General Brigham and the Department of Radiation Oncology at the Brigham. "This technique may be applied in many settings where patients get serial, longitudinal imaging, and we’re excited to see what this project will inspire."

Tak D, Garomsa BA, Zapaishchykova A, Ye Z, Vajapeyam S, Mahootiha M, Climent Pardo JC, Smith C, Familiar AM, Chaunzwa T, Liu KX, Prabhu S, Bandopadhayay P, Nabavizadeh A, Mueller S, Aerts HJ, Haas-Kogan D, Poussaint TY, Kann BH.
Longitudinal risk prediction for pediatric glioma with temporal deep learning.
NEJM AI, 2025. doi: 10.1056/AIoa2400703

Most Popular Now

Philips Foundation 2024 Annual Report: E…

Marking its tenth anniversary, Philips Foundation released its 2024 Annual Report, highlighting a year in which the Philips Foundation helped provide access to quality healthcare for 46.5 million people around...

New AI Transforms Radiology with Speed, …

A first-of-its-kind generative AI system, developed in-house at Northwestern Medicine, is revolutionizing radiology - boosting productivity, identifying life-threatening conditions in milliseconds and offering a breakthrough solution to the global radiologist...

Scientists Argue for More FDA Oversight …

An agile, transparent, and ethics-driven oversight system is needed for the U.S. Food and Drug Administration (FDA) to balance innovation with patient safety when it comes to artificial intelligence-driven medical...

New Research Finds Specific Learning Str…

If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto Area, differs from the real-world data, it could lead to patient harm...

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

Patients say "Yes..ish" to the…

As artificial intelligence (AI) continues to be integrated in healthcare, a new multinational study involving Aarhus University sheds light on how dental patients really feel about its growing role in...

Brains vs. Bytes: Study Compares Diagnos…

A University of Maine study compared how well artificial intelligence (AI) models and human clinicians handled complex or sensitive medical cases. The study published in the Journal of Health Organization...

'AI Scientist' Suggests Combin…

An 'AI scientist', working in collaboration with human scientists, has found that combinations of cheap and safe drugs - used to treat conditions such as high cholesterol and alcohol dependence...

Start-ups in the Spotlight at MEDICA 202…

17 - 20 November 2025, Düsseldorf, Germany. MEDICA, the leading international trade fair and platform for healthcare innovations, will once again confirm its position as the world's number one hotspot for...